Leveraging Multi-Faceted User Preferences for Improving Click-Through Rate Predictions

Recommender systems have been widely adopted by many firms across various industries over the past decade, as they could provide numerous economic benefits to the industry, such as influencing consumer choices, generating the lift in sales, and enhancing consumer trust. Many currently-deployed recommender systems primiarily optimize the similarity measure following the Collaborative Filtering paradigm during the recommendation performance, which focuses on the matching between targeted users and items of their interests. Although effective and useful in many cases, these still face several important challenges to fully address consumers’ concerns. In particular, they usually provide recommendations of the most similar items related to the consumption records, without taking into account the multi-faceted consumer preferences. For example, some consumers would be satisfied to receive recommendations that are novel and unexpected to broaden their horizons, while others might be interested to receive recommendations from other product categories to expand their interests. Falling to do so would lead to the filter bubble and exploration-exploitation trade-off phenomenon, which is undesirable because it would significantly reduce sales diversity and deteriorate user satisfaction with recommender systems.

In my thesis proposal, I will describe new methods to design recommender systems following two research streams to tackle the aforementioned challenges, namely unexpected recommender system and cross-domain system. In particular, unexpected recommender systems address consumers’ desire for variety, and aim at providing novel and useful recommendations simultaneously, while cross-domain recommender systems learn consumer preferences from their behaviors in other domains to better predict their behaviors in the target domain. The proposed models are capable of significantly improving recommendation performance and user satisfaction, as demonstrated through extensive offline and online experiments discussed in this proposal.